Summary of Overcoming Reward Overoptimization Via Adversarial Policy Optimization with Lightweight Uncertainty Estimation, by Xiaoying Zhang et al.
Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
by Xiaoying Zhang, Jean-Francois Ton, Wei Shen, Hongning Wang, Yang Liu
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs) is introduced, addressing the issue of reward over-optimization. The proposed Adversarial Policy Optimization (AdvPO) method quantifies uncertainties in rewards using last-layer embeddings of the reward model, then optimizes policy improvement within a confidence interval of the reward model’s predictions. Empirical results on Anthropic HH and TL;DR summarization datasets demonstrate AdvPO’s effectiveness in mitigating over-optimization, leading to enhanced performance when evaluated by humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re introducing a new way to help big language models learn from people’s feedback. Sometimes, these models get too good at following rules that aren’t exactly what people want. Our solution is called Adversarial Policy Optimization (AdvPO). It works by understanding how sure the model is about what it’s doing, and then making improvements based on that confidence. We tested AdvPO on some big datasets and showed that it helps models make better decisions that are more aligned with human preferences. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning from human feedback * Rlhf * Summarization
